{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:52:31Z","timestamp":1782993151974,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T00:00:00Z","timestamp":1516752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61331017"],"award-info":[{"award-number":["61331017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701478"],"award-info":[{"award-number":["61701478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.<\/jats:p>","DOI":"10.3390\/s18020334","type":"journal-article","created":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T09:47:36Z","timestamp":1516787256000},"page":"334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7546-5107","authenticated-orcid":false,"given":"Quanzhi","family":"An","sequence":"first","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China"},{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-3300","authenticated-orcid":false,"given":"Zongxu","family":"Pan","sequence":"additional","affiliation":[{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjian","family":"You","sequence":"additional","affiliation":[{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China"},{"name":"Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,24]]},"reference":[{"key":"ref_1","unstructured":"Crisp, D.J. (2004). The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery, DSTO Information Sciences Laboratory."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1080\/07038992.2001.10854896","article-title":"Automatic detection of ships in RADARSAT-1 SAR imagery","volume":"27","author":"Wackerman","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_3","unstructured":"Rye, A.J., Sawyer, F.G., and Sothinathan, R. (1990, January 20\u201324). A workstation for the fast detection of ships. Proceedings of the 10th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 1990), New York, NY, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ferrara, M.N., and Torre, A. (1998, January 6\u201310). Automatic moving targets detection using a rule-based system: Comparison between different study cases. Proceedings of the 18th IEEE International Geoscience and Remote Sensing Symposium, 1998 (IGARSS 1998), Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.691633"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0165-1684(96)00125-9","article-title":"Coastline detection by a Markovian segmentation on SAR images","volume":"55","author":"Descombes","year":"1996","journal-title":"Signal Process"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Peis, I., Ill\u00e1n, I.A., Mart\u00ednez-Murcia, F.J., Segovia, F., G\u00f3rriz, J.M., Ram\u00edrez, J., Lang, E.W., and Salas-Gonzalez, D. (November, January 29). MRI brain segmentation using hidden Markov random fields with alpha-stable distributions. Proceedings of the 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS\/MIC\/RTSD 2016), Strasbourg, France.","DOI":"10.1109\/NSSMIC.2016.8069422"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ji, K., Leng, X., Fan, Q., Zhou, S., and Zou, H. (2016, January 10\u201315). An land masking algorithm for ship detection in SAR images. Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729234"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Biamino, W., Borasi, M., Cavagnero, M., Croce, A., Matteo, L.D., Fontebasso, F., Tataranni, F., and Trivero, P. (2015, January 26\u201331). A \u2018dynamic\u2019 land masking algorithm for synthetic aperture radar images. Proceedings of the 35th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326783"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gu, D., and Xu, X. (2014, January 5\u20138). A novel procedure for land masking in ocean-land segmentation from SAR images. Proceedings of the 2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC 2014), Guilin, China.","DOI":"10.1109\/ICSPCC.2014.6986249"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"De Nicol\u00e1s, J.M., Moya, D.M., Amores, P.J., del Rey Maestre, N., and Humanes, J.L.B. (2013, January 5\u20137). Segmentation techniques for land mask estimation in SAR imagery. Proceedings of the 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN 2013), Madrid, Spain.","DOI":"10.1109\/CICSYN.2013.65"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Niharika, E., Adeeba, H., Krishna, A.S.R., and Yugander, P. (2017, January 19\u201320). K-means based noisy SAR image segmentation using median filtering and otsu method. Proceedings of the 2017 International Conference on IoT and Application (ICIOT 2017), Nagapattinam, India.","DOI":"10.1109\/ICIOTA.2017.8073630"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, A., Li, Y., Wang, T., and Niu, W. (2015, January 14\u201316). Medical image segmentation based on maximum entropy multi-threshold segmentation optimized by improved cuckoo search algorithm. Proceedings of the 2015 8th International Congress on Image and Signal Processing (CISP 2015), Shenyang, China.","DOI":"10.1109\/CISP.2015.7407926"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5053","DOI":"10.1080\/01431161.2014.933279","article-title":"A KPCA texture feature model for efficient segmentation of RADARSAT-2 SAR sea ice imagery","volume":"35","author":"Xu","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Symeonakis, E. (2016, January 10\u201315). Modelling land cover change in a Mediterranean environment using Random Forests and a multi-layer neural network model. Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730423"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 8\u201310). Fully convolutional networks for semantic segmentation. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_16","first-page":"473","article-title":"Ship Detection in GF-3 NSC Mode SAR Images","volume":"6","author":"Liu","year":"2017","journal-title":"J. Radas."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1016\/j.patcog.2006.01.019","article-title":"Fast detecting and locating groups of targets in high-resolution SAR images","volume":"40","author":"Gao","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5719","DOI":"10.1109\/TGRS.2017.2712700","article-title":"Analysis of distribution using graphical goodness of fit for airborne SAR sea-clutter data","volume":"55","author":"Xin","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4811","DOI":"10.1109\/TGRS.2017.2701813","article-title":"CFAR ship detection in nonhomogeneous sea clutter using polarimetric SAR data based on the notch filter","volume":"55","author":"Gao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Gigli, G., and Lampropoulos, G.A. (2002, January 24\u201328). A new maximum likelihood generalized gamma CFAR detector. Proceedings of the 22th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002), Toronto, ON, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1109\/TAES.2017.2672018","article-title":"Multimodel CFAR detection in foliage penetrating SAR images","volume":"53","author":"Izzo","year":"2017","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1049\/ip-rsn:19949886","article-title":"Analysis and comparison of two order statistics CFAR systems","volume":"141","author":"Galati","year":"1994","journal-title":"IEE Proc. Radar Sonar Navig."},{"key":"ref_23","unstructured":"Yu, Y., Huang, S.-J., and Torre, A. (1995, January 10\u201314). Development of an automatic target detection and characterisation system in polarimetric SAR images. Proceedings of the 15th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 1995), Firenze, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gagnon, L., Oppenheim, H., and Valin, P. (1998). R&D activities in airborne SAR image processing\/analysis at Lockheed Martin Canada. Proc. SPIE Int. Soc. Opt. Eng., 3251.","DOI":"10.1117\/12.328670"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1109\/TGRS.2015.2506822","article-title":"A segmentation-based CFAR detection algorithm using truncated statistics","volume":"54","author":"Tao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TGRS.2015.2451311","article-title":"Robust CFAR Detector Based on Truncated Statistics in Multiple-Target Situations","volume":"54","author":"Tao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4585","DOI":"10.1109\/TGRS.2013.2282820","article-title":"An improved iterative censoring scheme for CFAR ship detection with SAR imagery","volume":"52","author":"An","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1109\/LGRS.2010.2098434","article-title":"On the iterative censoring for target detection in SAR images","volume":"8","author":"Cui","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/TGRS.2008.2006504","article-title":"An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images","volume":"47","author":"Gao","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pan, Z., Liu, L., Qiu, X., and Lei, B. (2017). Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode. Sensors, 17.","DOI":"10.3390\/s17071578"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1080\/07038992.2001.10854872","article-title":"Integrating spaceborne SAR imagery into operational systems for fisheries monitoring","volume":"27","author":"Kourti","year":"2001","journal-title":"Can. J. Remote Sens."},{"key":"ref_32","first-page":"33","article-title":"Canadian progress toward marine and coastal applications of synthetic aperture radar","volume":"21","author":"Vachon","year":"2000","journal-title":"Johns Hopkins APL Tech. Dig."},{"key":"ref_33","unstructured":"Rey, M.T., Campbell, J., and Petrovic, D. (1998). A Comparison of Ocean Clutter Distribution Estimators for CFAR-Based Ship Detection in RADARSAT Imagery, Defence Research Establishment. Report No. 1340."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1109\/36.508418","article-title":"An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions","volume":"34","author":"Eldhuset","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1109\/LGRS.2016.2618604","article-title":"A Modified CFAR algorithm based on object proposals for ship target detection in SAR images","volume":"13","author":"Dai","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Iervolino, P., Guida, R., and Whittaker, P. (2015, January 1\u20134). A novel ship-detection technique for Sentinel-1 SAR data. Proceedings of the 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR 2015), Singapore.","DOI":"10.1109\/APSAR.2015.7306324"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Meng, W., Ju, T., and Yu, H. (2010, January 16\u201318). CFAR and KPCA for SAR image target detection. Proceedings of the 2010 3rd International Congress on Image and Signal Processing, Yantai, China.","DOI":"10.1109\/CISP.2010.5646813"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.10.012","article-title":"A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery","volume":"141","author":"Xu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, M., Xu, P., and Guo, Z. (2017, January 18\u201321). SAR ship detection using sea-land segmentation-based convolutional neural network. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958806"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kang, M., Leng, X., Lin, Z., and Ji, K. (2017, January 18\u201321). A modified faster R-CNN based on CFAR algorithm for SAR ship detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_41","unstructured":"Khesali, E., Enayati, H., Modiri, M., and Mohseni Aref, M. (2015, January 23\u201325). Automatic Ship Detection in Single-Pol Sar Images Using Texture Features in Artificial Neural Networks. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015 (ISPRS 2015), Kish Island, Iran."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, J., Qiu, X., and Hong, W. (2016, January 10\u201315). Automated ortho-rectified SAR image of GF-3 satellite using Reverse-Range-Doppler method. Proceedings of the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730158"},{"key":"ref_43","unstructured":"Nouar, N., and Farrouki, A. (2014, January 1\u20135). CFAR detection of spatially distributed targets in k-distributed clutter with unknown parameters. Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO 2014), Lisbon, Portugal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1117\/12.328668","article-title":"Comparison of probability statistics for automated ship detection in SAR imagery","volume":"Volume 3491","author":"Henschel","year":"1998","journal-title":"Proceedings of SPIE"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1049\/ip-rsn:19971107","article-title":"High resolution sea clutter data: Statistical analysis of recorded live data","volume":"144","author":"Farina","year":"1997","journal-title":"IEE Proc. Radar Sonar Navig."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1049\/ip-rsn:20010720","article-title":"Parameter estimation for the K-distribution based on [z log(z)]","volume":"148","author":"Blacknell","year":"2001","journal-title":"IEE Proc. Radar Sonar Navig."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1109\/7.805463","article-title":"Estimation of the parameters of the K-distribution using higher order and fractional moments [radar clutter]","volume":"35","author":"Iskander","year":"1999","journal-title":"IEEE Trans. Aerosp. Electron. Syst."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/334\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:25Z","timestamp":1760194345000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/2\/334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,24]]},"references-count":47,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["s18020334"],"URL":"https:\/\/doi.org\/10.3390\/s18020334","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,24]]}}}